EFL Students' Reactions to Peer versus Teacher Feedback to Improve Writing Skills: A Study at Intermediate School Level
Bibliographic record
Abstract
Feedback is crucial for assisting EFL writers since writing in English is challenging for them. Although numerous research studies have been done on the usefulness of peer and teacher feedback in EFL writing, studies that show the differences between the effectiveness of teacher's feedback versus peer's feedback and the student's reactions to mixing feedback are generally rare. This study was thus conducted on the peer and teacher feedback and both feedback model in three writing paragraphs for twenty students at an intermediate school in Buraydah, Saudi Arabia, where English is taught as a foreign language. To identify the students' reactions in the pre-post application of the questionnaire and the pre-post test design for one group of students, the study used a semi-experimental approach. The findings indicated no significant differences at a significance level of less than 0.05 between the mean scores of the peers and the teacher feedback. The experiment had success in terms of students’ positive attitudes towards mixing feedback models, the usefulness of peer comments, high percentages of feedback incorporations, and high overall writing scores. Therefore, based on the study results, the researcher confirms the usefulness of mixed feedback and recommends using it to improve student's' English writing skills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".